In addition, dif-ferences between KM and multiple decrement methods are pointed out.Finally, a real-data problem is used in order to illustrate the situation.
📌 3 Key Takeaways
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Objective & Hypothesis:Survival analysis has become in a common procedure in biomedical re-searches.
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Key Findings & Data:Conventionally, the well-known nonparametric Kaplan-Meier(KM) estimator is used in order to approximate the real survivor curve.However, in competing risk contexts where more than one failure causecompete to occur and only one of them is of interest, the direct useof the Kaplan-Meier statistic does not perform correctly and, in or-der to obtain a good estimation, it must be adapted.
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Conclusion & Impact:In addition, dif-ferences between KM and multiple decrement methods are pointed out.Finally, a real-data problem is used in order to illustrate the situation.
Survival analysis has become in a common procedure in biomedical re-searches. Conventionally, the well-known nonparametric Kaplan-Meier(KM) estimator is used in order to approximate the real survivor curve.However, in competing risk contexts where more than one failure causecompete to occur and only one of them is of interest, the direct useof the Kaplan-Meier statistic does not perform correctly and, in or-der to obtain a good estimation, it must be adapted. In this work,via Monte Carlo simulations, the author explores the behavior of theKaplan-Meier estimator in a competing risk context. In addition, dif-ferences between KM and multiple decrement methods are pointed out.Finally, a real-data problem is used in order to illustrate the situation.
Camblor, P. M. (2022). Kaplan-Meier estimator in competing risk contexts. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 4, 1161–1173. https://ajindex.com/articles/530059